This expansion pack extends the BMAD Method framework to support AI/ML engineering projects. It provides agents, workflows, templates, and best practices in a consolidated architecture.
Founder: Laurence Liew (@beowulf68) - Developed the initial workflows and agent framework. His contributions established the core methodology and approach.
Current Maintainers:
- Najib Ninaba (@najibninaba) - Core Team Member
- Siavash Sakhavi (@ssakhavi) - Core Team Member
BMAD frameworks support different project timelines:
- Traditional Timeline: 6-12 months
- BMAD Timeline: 3-12 weeks
- Iteration Speed: Prototyping and testing cycles
- Deployment Frequency: Multiple deployments per day
The streamlined AI/ML Engineering expansion pack provides specialized agents, workflows, templates, and best practices for:
- Machine Learning model development and deployment
- Large Language Model (LLM) and RAG application development
- Comprehensive MLOps pipeline implementation
- Unified AI security, ethics, and governance
- Data science and analytics workflows
- AI Singapore program-specific workflows
Your target project must be BMAD-enabled with instructions from BMAD-METHOD.
# Clone the expansion pack
git clone https://github.com/aisingapore/bmad-aisg-aiml.git
cd bmad-aisg-aiml2. Install the BMAD Pack Installer via uv
# Install once (recommended for regular use)
uv tool install bmad-pack-installer
# Or run directly without installation
uvx --from bmad-pack-installer bmad-pack-installer deploy . /path/to/project# Basic installation (from cloned repo directory)
bmad-pack-installer deploy . /path/to/project
# Preview changes without installing
bmad-pack-installer deploy . /path/to/project --dry-run
# Force reinstall over existing pack
bmad-pack-installer deploy . /path/to/project --force# Check if target is valid BMAD project
bmad-pack-installer check /path/to/project
# Validate this expansion pack (from cloned repo directory)
bmad-pack-installer validate .The installer creates:
- Hidden directory:
.bmad-aisg-aiml/ - Claude commands:
.claude/commands/bmadAISG/ - Updated manifests and symbolic links
-
Check workflows folder for workflow files:
# Navigate to installed workflows cd .bmad-aisg-aiml/workflows/ ls
-
For Claude Code implementation: Run the agents and tasks manually using
/{agent-name}command.
-
Marcus Tan Wei Ming - ML/AI Engineer & MLOps Specialist (
ml-engineer)- Heritage: Singaporean Chinese
- Expertise: End-to-end ML development, MLOps pipelines, infrastructure automation
- Technical Skills: PyTorch/TensorFlow, Kubernetes/Docker, CI/CD, cloud platforms
- Focus Areas: Model training, deployment, monitoring, production systems
-
Rizwan bin Abdullah - ML/AI System Architect (
ml-architect)- Heritage: Singaporean Malay
- Expertise: ML system design, scalable architectures, infrastructure planning
- Technical Skills: Distributed systems, transformer architectures, RAG systems
- Focus Areas: System design, model architecture selection, technical strategy
-
Sophia D'Cruz - Senior Data Scientist (
ml-data-scientist)- Heritage: Singaporean Eurasian
- Expertise: Statistical analysis, experimental design, recommendation systems
- Technical Skills: Causal inference, A/B testing, feature engineering
- Focus Areas: EDA, hypothesis testing, insights generation, model evaluation
-
Priya Sharma - ML Security & Ethics Specialist (
ml-security-ethics-specialist)- Heritage: Singaporean Indian
- Expertise: ML security, adversarial testing, AI ethics, compliance
- Technical Skills: Red teaming, bias detection, privacy protection
- Focus Areas: Security audits, ethical reviews, regulatory compliance
-
Dr. Dylan Poh - ML Research Scientist & Experimental Design Specialist (
ml-researcher)- Heritage: Singaporean Chinese
- Expertise: ML research planning, experimental design, literature review, hypothesis formulation
- Technical Skills: Advanced mathematics, ML frameworks, distributed computing, scientific writing
- Focus Areas: Research methodology, state-of-the-art ML techniques, reproducible experiments
Use previous agents as a template to create new agent folder:
Run in Claude Code (example):
Use agents/ml-architect.md as a template to create a ml-researcher agentInsert commands under "commands" in agent file:
commands:
- help: Show numbered list of the following commands to allow selection
- literature-review: use task create-research-doc.md with literature-review-tmpl.yaml- You should use formats like
use {task} with {template}orexecute {task}for the commands. (Refer to original bmad agents) create-research-doc.mdshould be placed in the tasks folderliterature-review-tmpl.yamlshould be placed in the templates folder
- Original BMAD has generic tasks like
create-docandadvanced-elicitationwhich are included in this package. - For complex tasks, generate your own task file
-
The installer copies some folders into
.claude/commandsfolder these are the files which/{command}run in bmad. -
/{agent-name}Run the prompt from the.claude/commands/bmad-expansion-name/agents/agent-namefile. -
To enable the agent to locate your file ensure the file path is inside the agent prompt. (This should be done by the installer)
-
This is a protion of the original prompt from a BMAD agent.
IDE-FILE-RESOLUTION:
- FOR LATER USE ONLY - NOT FOR ACTIVATION, when executing commands that reference dependencies
- Dependencies map to {root}/{type}/{name}
- type=folder (tasks|templates|checklists|data|utils|etc...), name=file-name
- Example: create-doc.md → .bmad-core/tasks/create-doc.md. #This line tells the agent where the hidden folder to check
- IMPORTANT: Only load these files when user requests specific command execution- ML Development: End-to-end model development process
- ML Deployment: Production deployment with monitoring
- ML Experimentation: Systematic experimentation framework
| Program | Duration | Team Structure | Deliverable | Key Difference |
|---|---|---|---|---|
| MVP | 6 months | 1 AI Engineer + 2-6 Apprentices | Full production system | Comprehensive with training |
| POC | 3 months | 1 AI Engineer + 2-4 Apprentices | Proof of concept | Feasibility with training |
| SIP | 3 months | 1-2 AI Engineers (NO apprentices) | Production MVP | Fast delivery, no training |
| LADP | 4 months | Learners + Mentors (guide only) | LLM application | Self-directed learning |
- Team: 1 AI Engineer + 2-6 Apprentices
- Objective: Build comprehensive production system with apprentice training
- Phases: Discovery → Experimentation → Productionization → Validation
- All 4 agents activated across phases
- Team: 1 AI Engineer + 2-4 Apprentices
- Objective: Validate technical feasibility and business value
- Phases: Rapid Discovery → Prototyping → Deployment → Validation
- All 4 agents for comprehensive validation
- Team: 1-2 AI Engineers only (NO apprentices)
- Objective: Deliver production MVP without training overhead
- Phases: Discovery → Development → Productionization → Handover
- All 4 agents for fast MVP delivery
- Duration: 4 months part-time (8-10 hrs/week) or 1-3 days full-time
- Team: Learners with mentor guidance (mentors guide but DON'T code)
- Objective: Build real-world LLM applications with company SOW
- Structure: Month 1 (Self-learning) → Month 2 (Design) → Month 3 (Development) → Month 4 (Deployment)
- 3 workshops + project implementation
- MVP: Production systems completed in 6 months with training
- POC: Proof of concepts completed in 3 months with learning
- SIP: Production MVPs completed in 3 months
- LADP: LLM applications developed in 4 months
graph TD
A[Start: AI/ML Project] --> B[ml-architect: aiml-brief.md]
B --> C[ml-researcher: literature-review.md]
B --> D[ml-architect: aiml-design-document.md]
C --> D
D --> E[ml-architect: aiml-architecture.md]
E --> F[ml-architect: user-stories.md]
F --> G[ml-architect: shard documents]
G --> H[ml-engineer: create story]
H --> I[ml-engineer: validate story]
I -->|Yes| H
I -->|No| J[user: provide feedback]
J --> H
%% Styling with black font and unique colors for each agent
style A fill:#E8F5E8,color:#000000,stroke:#000000
style B fill:#FFE4E1,color:#000000,stroke:#000000
style C fill:#E6F3FF,color:#000000,stroke:#000000
style D fill:#FFE4E1,color:#000000,stroke:#000000
style E fill:#FFE4E1,color:#000000,stroke:#000000
style F fill:#FFE4E1,color:#000000,stroke:#000000
style G fill:#FFE4E1,color:#000000,stroke:#000000
style H fill:#FFF2CC,color:#000000,stroke:#000000
style I fill:#FFF2CC,color:#000000,stroke:#000000
style J fill:#F0E68C,color:#000000,stroke:#000000
%% Color legend for agents:
%% ml-architect (Rizwan): #FFE4E1 (Light Coral)
%% ml-engineer (Marcus): #FFF2CC (Light Yellow)
%% ml-data-scientist (Sophia): #E6F3FF (Light Blue)
%% ml-security-ethics-specialist (Priya): #E8F8E8 (Light Green)
All agents include:
- Local regulatory knowledge: PDPA, IMDA, MAS
- AISG program experience: MVP, POC, SIP, LADP workflows
- Understanding of local market dynamics: Singapore tech ecosystem
- Government standards compliance: National AI governance standards
bmad-ai-ml-engineering/
├── agents/ # 5 core agents
│ ├── ml-engineer.md
│ ├── ml-architect.md
│ ├── ml-data-scientist.md
│ ├── ml-security-ethics-specialist.md
│ └── ml-researcher.md
├── agent-teams/ # 5 team configurations
├── checklists/ # 4 checklists
├── templates/ # 8 templates
├── tasks/ # 5 tasks
├── workflows/ # Standard + 4 AISG workflows
├── data/ # 2 reference files
├── web-bundles/ # 5 ready-to-use bundles
└── config.yaml # Configuration
- ✅ Required: bmad-core >= 4.0.0
- 🔧 Recommended: Python >= 3.8, Docker, Kubernetes
- ➕ Optional: Terraform, MLflow, Kubeflow
- PDPA: Personal Data Protection Act compliance
- IMDA: Model AI Governance Framework aligned
- MAS FEAT: Fairness, Ethics, Accountability, Transparency
- ISO/IEC 23053: Framework for AI using ML
- ISO/IEC 23894: AI risk management
Contribution process:
- Core Team: Direct commit access for maintenance and development
- External Contributors: Submit contributions via pull requests
- Review Process: All PRs require approval from core team members
See our Contributing Guidelines for detailed information on how to contribute.
For a complete list of contributors, see CONTRIBUTORS.md.
- Quick Start: This README
- Workflows:
/workflows/README.md - Web Bundles:
/web-bundles/WEB-BUNDLE-INSTRUCTIONS.md - Agents: Individual agent files in
/agents/
- Review
REFACTORING-SUMMARY.mdfor v2.0 changes - Check agent-specific documentation
- Consult workflow guides
- Raise issues in the repository
- 5 core agents (added ml-researcher)
- Added SIP workflow for MVP delivery
- Updated LADP to 4-month programme
- Added Singapore context
- Initial release
- Basic AISG workflows
